The present disclosure is related to the field of analytics. More specifically, analytics are used to identify, provide, and evaluate personalized guidance in a monitored setting.
Current real-time agent assistant applications are driven by predefined scenarios and static rules that are triggered by desktop and/or speech events together with historical data to suggest a course of action or show some type of guidance to an agent.
Off-line training and coaching solutions are time intensive, and by their very nature, must be generalized to the experience of an average agent or commonly encountered issues. Still even with current assistance, training, and coaching techniques, agents may experience individual lapses in quality, or agents may be slow to adopt new or changing interaction instructions. In still further instances, increased segmentation and granularization of responses or expectations for interactions with specific individuals can create a multitude of responses or procedures which can be difficult for even experienced agents to remember and effectively carry out.
Therefore, as disclosed herein, by using an in-depth understanding of how activities are carried out, guidance for individuals, groups, or specific activities can be automatedly added or removed.